Energy Efficiency Trade-Off Between Duty-Cycling and Wake-Up Radio Techniques in IoT Networks

Kozłowski, Adam; Sosnowski, J.

doi:10.1007/s11277-019-06368-0

Cited by 62 publications

(29 citation statements)

References 40 publications

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“…Whereas the distance between the source and destination node is not considered which increases latency. In (Kozłowski & Sosnowski, 2019) the EEDC algorithm provides a cycling transmitting and receiving process. The suggested process augments the delay of packet data, which inclines to loss data.…”

Section: Related Workmentioning

confidence: 99%

A dynamic heuristic for WSNs routing

Tounsi

2021

Cogent Engineering

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One of the most important issues in the wireless sensor networks (WSNs) is nodes energy. The routing protocols as well as nodes management are considered as the key elements to improve a network's lifetime. In this research, we have suggested a dynamic heuristic for new nodes selection that manages efficiently the active nodes using a Tabu Node Selection coupled with minimum spanning tree routing protocol (TNS). Minimizing the total energy consumption in WSN, is not enough alone, since some nodes drain their energy faster than others, and may cause network failure. To balance the energy consumption, our strategy is to identify nodes with high-energy depletion and balance data routing on their neighbours. This approach performs nodes energy balancing, by creating lists of Tabu nodes (periodically inactive nodes). Such a scheme will prevent the most used nodes, such as nodes close to the base station to see their energy drain. The nodes in the Tabu lists will dynamically be active according to the energy level of nodes surrounding them. Our new dynamic heuristic has proven to outperform other nodes management approach which does not use Tabu search. The experiments show that Tabu list combined with Minimum Spanning Tree routing protocol extend drastically the network lifetime by balancing optimally the nodes energy.

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Section: Related Workmentioning

confidence: 99%

A dynamic heuristic for WSNs routing

Tounsi

2021

Cogent Engineering

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“…The Energy-Efficient Duty Cycling (EEDC) algorithm is proposed in the wireless sensor nodes [31][32][33]. It balances both the delay and duty cycling of the sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling

Sinde

Begum

Njau³

et al. 2020

Sensors

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Over the recent era, Wireless Sensor Network (WSN) has attracted much attention among industrialists and researchers owing to its contribution to numerous applications including military, environmental monitoring and so on. However, reducing the network delay and improving the network lifetime are always big issues in the domain of WSN. To resolve these downsides, we propose an Energy-Efficient Scheduling using the Deep Reinforcement Learning (DRL) (E2S-DRL) algorithm in WSN. E2S-DRL contributes three phases to prolong network lifetime and to reduce network delay that is: the clustering phase, duty-cycling phase and routing phase. E2S-DRL starts with the clustering phase where we reduce the energy consumption incurred during data aggregation. It is achieved through the Zone-based Clustering (ZbC) scheme. In the ZbC scheme, hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithms are utilized. Duty cycling is adopted in the second phase by executing the DRL algorithm, from which, E2S-DRL reduces the energy consumption of individual sensor nodes effectually. The transmission delay is mitigated in the third (routing) phase using Ant Colony Optimization (ACO) and the Firefly Algorithm (FFA). Our work is modeled in Network Simulator 3.26 (NS3). The results are valuable in provisions of upcoming metrics including network lifetime, energy consumption, throughput and delay. From this evaluation, it is proved that our E2S-DRL reduces energy consumption, reduces delays by up to 40% and enhances throughput and network lifetime up to 35% compared to the existing cTDMA, DRA, LDC and iABC methods.

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“…Two factors significantly affect the power consumption of battery-powered IoT devices and sensors: radio transmissions, and channel access. However, the amount of power consumed by the MAC layer can be reduced, e.g., by reducing the amount of idle listening, duty cycling [ 52 ], implementing sleep scheduling [ 53 ]; and assigning priority slots for low-power devices [ 54 ].…”

Section: Design Considerations For Mac Protocols Of Uiotmentioning

confidence: 99%

Medium Access Control Protocols for the Internet of Things Based on Unmanned Aerial Vehicles: A Comparative Survey

Khisa

Moh

2020

Sensors

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The Internet of Things (IoT), which consists of a large number of small low-cost devices, has become a leading solution for smart cities, smart agriculture, smart buildings, smart grids, e-healthcare, etc. Integrating unmanned aerial vehicles (UAVs) with IoT can result in an airborne UAV-based IoT (UIoT) system and facilitate various value-added services from sky to ground. In addition to wireless sensors, various kinds of IoT devices are connected in UIoT, making the network more heterogeneous. In a UIoT system, for achieving high throughput in an energy-efficient manner, it is crucial to design an efficient medium access control (MAC) protocol because the MAC layer is responsible for coordinating access among the IoT devices in the shared wireless medium. Thus, various MAC protocols with different objectives have been reported for UIoT. However, to the best of the authors’ knowledge, no survey had been performed so far that dedicatedly covers MAC protocols for UIoT. Hence, in this study, state-of-the-art MAC protocols for UIoT are investigated. First, the communication architecture and important design considerations of MAC protocols for UIoT are examined. Subsequently, different MAC protocols for UIoT are classified, reviewed, and discussed with regard to the main ideas, innovative features, advantages, limitations, application domains, and potential future improvements. The reviewed MAC protocols are qualitatively compared with regard to various operational characteristics and system parameters. Additionally, important open research issues and challenges with recommended solutions are summarized and discussed.

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Energy Efficiency Trade-Off Between Duty-Cycling and Wake-Up Radio Techniques in IoT Networks

Cited by 62 publications

References 40 publications

A dynamic heuristic for WSNs routing

A dynamic heuristic for WSNs routing

Refining Network Lifetime of Wireless Sensor Network Using Energy-Efficient Clustering and DRL-Based Sleep Scheduling

Medium Access Control Protocols for the Internet of Things Based on Unmanned Aerial Vehicles: A Comparative Survey

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